Route Recommendation Research Articles

Research on personalised recommendation method of popular tourist attractions routes based on machine learning

In view of the problems existing in the current tourist route recommendation methods, this paper proposes a personalised tourist attraction route recommendation method based on machine learning. The tourism heterogeneous information data is collected by using HTML parser and the captured data are used as machine learning training samples. The characteristics and user interest characteristics under the rating were extracted, and the target user interest characteristics were taken as the starting point, combining the features of scenic spots and tourist routes. Recommend scenic spots to users to realise personalised recommendation of popular scenic spots. The experimental results show that the method proposed in this paper has a recommendation accuracy of more than 95%, a recommendation time of between 115 ms-130 ms and a user satisfaction rate of over 89%. With high recommendation accuracy and user satisfaction, and less time to calculate the recommendation, it is a reliable method to recommend tourist routes.

Practice Patterns and Training Needs Among Physicians Certifying Patients for Medical Marijuana in Florida.

Background:Little is known about the clinical training or practice experiences among physicians who certify patients for medical marijuana. The objective of this study was to determine information sources, factors influencing recommendations, clinical practices in patient assessment, communications, and recommendations, and priority areas for additional training among physicians who certify patients for medical marijuana.Methods:A cross-sectional state-wide anonymous survey of registered medical marijuana physicians in Florida between June and October 2020 was administered. Numerical responses were quantified using counts and percentages. The frequencies for “often” and “always” responses were aggregated when appropriate.Results:Among 116 respondents, the mean (standard deviation) age was 57 (12) years old, and 70% were male. The most frequently used information sources were research articles (n = 102, 95%), followed by online sources (n = 99, 93%), and discussions with other providers and dispensary staff (n = 84, 90%). Safety concerns were most influential in patient recommendations (n = 39, 39%), followed by specific conditions (n = 30, 30%) and patient preferences (n = 26, 30%). Ninety-three physicians (92%) reported they “often” or “always” perform a patient physical exam. Eighty-four (77%) physicians provided specific administration route recommendations. Half (n = 56) “often” or “always” provided specific recommendations for Δ-9-tetrahydrocannabinol: cannabidiol ratios, while 69 (62%) “often” or “always” provided specific dose recommendations. Online learning/training modules were the most preferred future training mode, with 88 (84%) physicians “likely” or “very likely” to participate. The top 3 desired topics for future training were marijuana-drug interactions (n = 84, 72%), management of specific medical conditions or symptoms (n = 83, 72%), and strategies to reduce opioids or other drugs use (n = 78, 67%).Conclusions:This survey of over 100 medical marijuana physicians indicates that their clinical practices rely on a blend of research and anecdotal information sources. While physicians report clinical factors as influential during patient recommendation, patient assessment practices and treatment regimen recommendations vary substantially and rely on experimental approaches. More research is needed to inform evidence-based practice and training, especially considering details on drug interactions, risk-benefit of treatment for specific clinical conditions, and strategies to reduce opioid use.

Discovering Travel Spatiotemporal Pattern Based on Sequential Events Similarity

Travel route preferences can strongly interact with the events that happened in networked traveling, and this coevolving phenomena are essential in providing theoretical foundations for travel route recommendation and predicting collective behaviour in social systems. While most literature puts the focus on route recommendation of individual scenic spots instead of city travel, we propose a novel approach named City Travel Route Recommendation based on Sequential Events Similarity (CTRR-SES) by applying the coevolving spreading dynamics of the city tour networks and mine the travel spatiotemporal patterns in the networks. First, we present the Event Sequence Similarity Measurement Method based on modelling tourists’ travel sequences. The method can help measure similarities in various city travel routes, which combine different scenic types, time slots, and relative locations. Second, by applying the user preference learning method based on scenic type, we learn from the user’s city travel historical data and compute the personalized travel preference. Finally, we verify our algorithm by collecting data of 54 city travellers of their historical spatiotemporal routes in the ten most popular cities from Mafeng.com. CTRR-SES shows better performance in predicting the user’s new city travel sequence fitting the user’s individual preference.

Intelligent Tourism Recommendation Algorithm based on Text Mining and MP Nerve Cell Model of Multivariate Transportation Modes

Currently, the recommendation method on tourist sight and tour route lacks of the mechanism of tourists’ interests mining and the precise tourist sights recommending, and the planned tour routes cannot properly and adequately combine with the real world environment. Meanwhile, the research on the multivariate transportation modes in tour route recommendation is not sufficient. Aim at the problems of the tourist sight and tour route recommendation in intelligent tourism recommendation system of the intelligent tourism construction, this paper brings forward a tourism recommendation algorithm based on text mining and MP nerve cell model of multivariate transportation modes. The research specially focuses on the optimal tourist sight matching algorithm based on tourists’ interests mining and the optimal tour route chain algorithm based on the multivariate transportation modes. First, it analyzes the problems on tourism recommendation, based on which, the tourist sight clustering algorithm on feature attribute label and the tourist sight text mining algorithm on interest label are developed. The mined tourist sights will approach tourists’ interests to the maximum extent. Secondly, Considering the critical impact of the selected transportation mode on motive benefit satisfaction in the tour route chain, the tour route chain algorithm based on the nerve cell model of multivariate transportation modes is developed. This algorithm combines with geographic information element and transportation element, and it simulates the bionic principle of input and output information process on MP nerve cell, then the tour route chain model based on the nerve cell of multivariate transportation modes is set up. Through the iteration of multiple layer nerve cell motive weight values and accommodation coefficients, the algorithm finally outputs the signal information flow motive values, in which the tour route chain with the maximum information flow motive value is generated. Thirdly, to testify the feasibility and practicalness of the algorithm, an experimental example in real-world environment is designed and performed. The feasible matched tourist sights and tour route chains are output, meanwhile, the three commonly used optimal route searching algorithms are set as the control group, and along with the developed algorithm, they are compared with each other on the aspect of optimal tour route chain. The experiment testifies that the developed algorithm is feasible and practical, and has advantages on the tourism recommendation. Through the algorithm design and the experiment, it finds that the mined tourist sights by the objective function in the algorithm can best match tourists’ interest labels. The algorithm adequately combines with the real world tourism data of the geographic information, traffic information and tourist sight information and outputs the tour routes that best match tourists’ interests. Compare with the control group algorithms, the tour routes output by the developed algorithm have the highest motive satisfaction, lowest time complexity and space complexity. The developed algorithm is mainly used as the embedded algorithm for the intelligent tourism recommendation system, whose direct aim is to provide service for the tourists. Meanwhile, it can also provide service for the tourism administrations to collect, manage and mine the interest data as well as discover knowledge, and help the government to optimize the urban transportation system, launch the public vehicles and optimize the transportation guarantee strategy.

Analysis of driver’s choice behavior based on the combined model of utility-regret

In the context of increasing urban traffic trajectory data, based on large-scale urban traffic trajectory data, in the field of intelligent transportation, it has become an urgent need to analyze travelers’ routing behaviors and establish effective routing models to provide travelers with efficient and reasonable driving route recommendations. It is often the case that urban taxi groups has better route choice behavior than other travel group under the cost-benefit constraints. Therefore, providing route recommendations for other travelers according to the route choice habits of urban taxi group is a feasible solution to the above need. This paper starts with the utility maximization model and the stochastic regret minimization model, for the problem that the traveler cannot satisfy the premise of the complete rationality of the utility maximization model when choosing routes and the random regret minimization model ignores the traveler’s sensitivity to the original value of the alternative attribute, and improve the calculation formula of the regret value in the random regret minimization model. A route choice model under the utility-regret combination rule is proposed based on the combination of the utility model and the improved regret model, and then, based on the historical trajectory data of taxis in Xi’an, the RP data required by the model is extracted, and the parameters of the model are estimated and verified. The experimental results show that the route choice probability calculated by the model is closer to the route choice probability in the real scene than the simple utility maximization model or the simple random regret minimization model.

A Force-Directed Approach to Seeking Route Recommendation in Ride-on-Demand Service Using Multi-Source Urban Data

The rapidly-growing business of ride-on-demand (RoD) service such as Uber, Lyft and Didi proves the effectiveness of their new service model – using mobile apps and dynamic pricing to coordinate between drivers, passengers and the service provider, to manipulate the supply and demand, and to improve service responsiveness as well as quality. Despite its success, dynamic pricing creates a new problem for drivers: how to seek for passengers to maximize revenue under dynamic prices. Seeking route recommendation has already been studied extensively in traditional taxi service, but most studies do not consider the effects of taxis and passengers on the seeking taxi simultaneously. Further, in RoD service it is necessary to consider more factors such as dynamic prices, the status of other transportation services, etc. In this paper, we employ a force-directed approach to model, by analogy, the relationship between vacant cars and passengers as that between positive and negative charges in electrostatic field. We extract features from multi-source urban data to describe dynamic prices, the status of RoD, taxi and public transportation services, and incorporate them into our model. The model is then used in route recommendation in every intersection so that a driver in a vacant RoD car knows which road segment to take next. We conduct extensive experiments based on our multi-source urban data, including RoD service operational data, taxi GPS trajectory data and public transportation distribution data, and results not only show that our approach outperforms existing baselines, but also justify the need to incorporate multi-source urban data and dynamic prices.

A study on topic models using LDA and Word2Vec in travel route recommendation: focus on convergence travel and tours reviews

At present, we live in prosperity contrary to the past times. As income increases, people enjoy wealth, but more people tend to pursue their own inner happiness: travel. People go to other places or visit foreign countries for business or journey. This study aims to identify the best tour route for foreign tourists in South Korea. Based on the review analysis results, this paper also aims to put forward techniques and methodologies required in practical affairs when developing a travel site or a travel application. On this note, it collected tourists’ reviews at the Tripadvisor official website and conducted text mining technique as well as network analysis using R and Tagxedo, which is a big data analytic tool. The analysis results displayed that there were differences in travel preference, and especially, individual travelers had difficulty traveling by public transportation and selecting travel destinations. Therefore, customized travel routes were suggested for convenient use among travelers.

Research on Construction of a Cloud Platform for Tourism Information Intelligent Service Based on Blockchain Technology

The development of global tourism has put forward new requirements for the construction of smart tourism. More and more travel-related data have reached the level of TB or even PB, which has brought great difficulties to tourism management. This article explores the use of big data technologies such as genetic algorithms to explore massive travel data and establish a comprehensive tourism information service platform for governments, enterprises, tourists, and scientific research institutions. The overall design of an industrial information service platform based on big tourist data is proposed. The overall function, data source, data standard, and application scope of the platform are all focused on. The traceability and nontampering of blockchain technology can also help passengers retain and verify their identity information. From the perspective of the design goals of the system, in general, the time required for repeated authentication will greatly reduce air ticket bookings, accommodation reservations, and bill verification, and improving efficiency is the only way to establish a “trust ecology.” Architecture design, distributed architecture, and intelligent service design, as well as the key implementation technology of service platform construction, route recommendation algorithm and tourism information big data mining, research and analysis on the construction of tourism information intelligent service.

Serendipity-based Points-of-Interest Navigation

Traditional venue and tour recommendation systems do not necessarily provide a diverse set of recommendations and leave little room for serendipity . In this article, we design MPG, a Mobile Personal Guide that recommends: (i) a set of diverse yet surprisingly interesting venues that are aligned to user preferences and (ii) a set of routes, constructed from the recommended venues. We also introduce EPUI, an Experimental Platform for Urban Informatics. Our comparison with the state-of-the-art schemes indicates that MPG is capable of providing high-quality venues and route recommendations while incorporating seamlessly both the notion of diversity and that of serendipity.

ESCORT: Fine-Grained Urban Crime Risk Inference Leveraging Heterogeneous Open Data

Crime has been a complex social problem worldwide, impacting numerous individuals in both property and psychology, and affecting public safety as well. To prevent and avoid crime is of great importance for urban authorities and citizens. Crime prediction using various urban sensing data provides a promising paradigm to cope with this challenging problem. However, previous studies on crime prediction mainly focus on coarse-grained prediction at the region level, which fail in fine-grained road-level crime inference due to data sparsity problems. This article proposes a road-level framework for fine-grained crime risk inference leveraging heterogeneous open data. We first extract a set of features from relevant external datasets and historical crime records, i.e., temporal, spatial, and recurrence crime features. We then establish a spatio-temporal crime pattern using spatio-temporal features, and further infer road-level crime risk incorporating with recurrence crime features. The framework not only considers a useful spatio-temporal pattern but also considers a unique crime near-repeat phenomenon, which helps address the data sparsity. We further present an application crime risk-aware route recommendation to demonstrate the effectiveness of our framework. This application cannot be solved well with coarse-grained (e.g., region-level) crime risk results since it is inconvenient for people to travel while avoiding large regions with potential crime risks. Experiments with the real-world datasets from New York City show that our framework accurately infers road-level crime risk and outperforms other baseline methods.

Availability of film studios for the visually impaired people

Problem statement. The article examines the existing and experimental methods of matching architectural spaces with the needs of visually impaired people and identifies the most relevant approaches and ways in this context for the modernization of premises, facilities and open areas located in existing state and semi-state studios of Ukraine. The film studio is a complex industry facility, which involves hundreds of professionals, and which involves periodical visits by temporary workers and tour groups. Therefore, architectural structures and open areas of the film studio space need to be adapted for the use of people with disabilities, including the visually impaired. Purpose of the article is to determine the most rational methods of adapting the spaces of film studios to the needs of visually impaired people, given the cultural and recreational significance of such complexes. The aims of the study include: analysis of existing approaches to adapting the architectural space to the needs of visually impaired people; classification of activity features of film studios in the context of interaction with people with visual impairments; identification of rational ways of modernization of film studio spaces regarding their adaptation to the needs of visually impaired people. Conclusion. Based on the analysis of existing adaptation methods of architectural spaces to the needs of visually impaired people, a number of rational approaches to modernization of the environment of Ukrainian state and semi-state film studios have been identified in the context of their increased cultural and recreational capacity. Recommendations for a special walking route on the territory of Oleksandr Dovzhenko National Film Studio for sightseeing groups of visually impaired people have been created based on the principles of non-visual perception.

Sistem Fleet Management Distribusi Menggunakan Metode Saving Matrix

Distribution is the activity of distributing goods from producers to consumers. Companies are obliged to optimize distribution so that they can easily compete with other companies, this can be done by optimizing the fleet. Each fleet must obtain information including the distance traveled, objectives achieved, periodic maintenance and service planning. PT Kino has 30 fleets, there is a problem where the estimated delivery time cannot be monitored because the company does not have a route plan in each shipment, this results in route optimization with the layout of goods in an unstructured fleet, from these problems the company does not have schedule reports and route planning data optimal for the achievement of the company. Another problem is that it is difficult for companies to monitor the fleet during the delivery of goods, this is because the ongoing process is still carried out in a minimalist manner which results in different reporting data between the customer and the delivery bag. The solution to overcome this problem was created a Fleet Management System using the concept of the Saving Matrix used to determine the distance and route of goods delivery from the company to consumers in order for the delivery of goods to be carried out in an effective and efficient manner. The software development method used is the waterfall. The results of this study are in the form of a system that has outputs including displaying route recommendations and monthly or yearly distribution schedule reports. After building the system, the user acceptance test was carried out with a result of 83.95% of the 41 test cases conducted by 6 actors.

Safe Route Recommendation System for Pedestrians

Safe Route Recommendation System for Pedestrians

Impact on Road Safety and Operation of Rerouting Traffic in Rural Travel Time Information System.

The Travel Time Information System (TTIS) is an Intelligent Traffic Control System installed in Poland. As is common, travel time is the only factor in the decision about rerouting traffic, while a route recommendation may consider multiple criteria, including road safety. The aim of the paper is to analyze the safety level of the entire road network when traffic is rerouted on paths with different road categories, intersection types, road environments, and densities of access points. Furthermore, a comparison between traffic operation and road safety performance was carried out, considering travel time and delay, and we predicted the number of crashes for each possible route. The results of the present study allow for maximizing safety or traffic operation characteristics, providing an effective tool in the management of the rural road system. The paper provides a methodology that can be transferred to other TTISs for real-time management of the road network.

Spatio-temporal feature fusion for dynamic taxi route recommendation via deep reinforcement learning

Dynamic taxi route recommendation aims at recommending cruising routes to vacant taxis such that they can quickly find and pick up new passengers. Given citizens’ giant but unbalancing riding demand and the very limited taxis in a city, dynamic taxi route recommendation is essential for its ability to alleviate the waiting time of passengers and increase the earning of taxi drivers. Thus, in this paper we study the dynamic taxi route recommendation problem as a sequential decision-making problem and we design an effective two-step method to tackle it. First, we propose to consider and extract multiple real-time spatio-temporal features, which are related with the easiness degree of vacant taxis picking up new passengers. Second, we design an adaptive deep reinforcement learning method, which learns a carefully designed deep policy network to better fuse the extracted spatio-temporal features such that effective route recommendation can be done. Extensive experiments using real-world data from San Francisco and New York are conducted. Comparing with the state-of-the-arts, our method can increase at least 15.8% of average earning for taxi drivers and reduce at least 29.6% of average waiting time for passengers.

Corridor Detection from Large GPS Trajectories Datasets

Given the widespread use of mobile devices that track their geographical location, it has become increasingly easy to acquire information related to users’ trips in real time. This availability has triggered several studies based on user’s position, such as the analysis of flows of people in cities, and also new applications, such as route recommendation systems. Given a dataset of geographical trajectories in an urban metropolitan area, we propose a new algorithm to detect corridors. Corridors can be defined as geographical paths, with a minimum length, that are commonly traversed by a minimum number of different users. We propose an efficient strategy based on the Apriori algorithm to extract frequent trajectory patterns from the geo-spatial dataset. By discretizing the data and adapting the roles of itemsets and baskets of this algorithm to our context, we find the longest corridors formed by cells shared by a minimum number of trajectories. After that, we refine the results obtained with a subsequent filtering step, by using a Radius Neighbors Graph. To illustrate the algorithm, the GeoLife dataset is analyzed by following the proposed method. Our approach is relevant for transportation analytics because it is the base to detect lacking lines in public transportation systems and also to recommend to private users which route to take when moving from one part of the city to another on the basis of behavior of the users who provided their logs.

Whom to Follow? A Comparison of Walking Routes Computed Based on Social Media Photos from Different Types of Contributors

Abstract. Since many tourists share the photos they take on social media channels, large collections of tourist attraction photos are easily accessible online. Recent research has dealt with identifying popular places from these photos, as well as computing city tourism routes based on these photo collections. Although current approaches show great potential, many tourism attractions suffer from being overrun by tourists, not least because many tourists are aware of only a few tourism hot spots that are trending. In the worst case, automatic city route recommendations based on social media photos will intensify this issue and disappoint tourists who seek individual experiences. In the best case, however, if individual preferences are appropriately incorporated into the route planning algorithm, more personalized route recommendations will be achieved. In this paper, we suggest distinguishing two different types of photo contributors, namely: first-time visitors who are usually tourists who "follow the crowd" (e.g., to visit the top tourist attractions), and repeated visitors who are usually locals who "don’t follow the crowd" (e.g., to visit photogenic yet less well-known places). This categorization allows the user to decide how to trade the one objective off against the other. We present a novel method based on a classification of photographers into locals and tourists, and show how to incorporate this information into an algorithmic routing framework based on the Orienteering Problem approach. In detailed experiments we analyze how choosing the parameter that models the trade-off between both objectives influences the optimal route found by the algorithm, designed to serve the user’s travel objective and preferences in terms of visited attraction types.

A Traffic Density Estimation Model Based on Crowdsourcing Privacy Protection

Acquiring traffic condition information is of great significance in transportation guidance, urban planning, and route recommendation. To date, traffic density data are generally acquired by road sound analysis, video data analysis, or in-vehicle network communication, which are usually financially or temporally expensive. Another way to get traffic conditions is to collect track data by crowdsourcing. However, this way lead to a greater risk of leaking users’ privacy. To avoid the risk, this article proposes a traffic density estimation model based on crowdsourcing privacy protection. First, in the acquisition process of the track data by crowdsourcing, dual servers are employed for transmission, and homomorphic encryption is carried out to encrypt the data to protect the data from being leaked during transmission. Second, sampling is implemented for randomization and anonymization to reduce the spatial continuity and temporal continuity of position data. In this way, the intermediate server cannot acquire users’ original data, and the main server cannot obtain users’ personal information. Finally, before data transmission, Laplace noising is performed on the users’ local position data to further protect the original location information. The proposed algorithm in this study realizes that only users have their original track data, and the servers involved in the work cannot infer the original track data, which ensures the real security of user privacy. The proposed algorithm was verified with the track data from the Didi Gaia Data Opening Plan. The experimental results showed that the proposed algorithm could still maintain the validity of data analysis results and the security of user data privacy after homomorphic encryption, noise addition, and sample collection, and displayed good robustness and scalability.

Real-Time Route Recommendations for E-Taxies Leveraging GPS Trajectories

Electric vehicles (EVs) currently face formidable challenges in promotion, i.e., short driving ranges, long charging times, and few charging stations, thereby limiting their acceptability to taxi drivers. Leveraging massive-scale taxi GPS trajectory data, we present a novel real-time route recommendation system for electric taxi (ET) drivers. Taxi travel knowledge, including the probability of picking up passengers and the distribution of destinations, is learned from the raw GPS trajectories. Considering the cascading effect of route decision making, consecutive ET actions are modeled with an action tree. The corresponding expected net revenue is estimated based on the learned knowledge. A prototype online system is developed for providing route recommendations, e.g., when to go to a charging station or cruise on certain roads. An experiment in Shenzhen demonstrates that the average daily net revenue of ET drivers is better than those of 76.2% of gasoline taxi drivers. The presented approach not only increases the revenue of ET drivers in the short term but also improves the viability of EVs in the long run.

Multi-Modal Route Recommender System for Bangkok Public Transportation

Currently, Bangkok has a 151 kilometers service of a rail line, whereas the total plan is 540 kilometers. More rail lines are now under construction and supposed to be done by a few years. Regarding a massive public transportation network, we need a route recommender system to make traveling more efficient. This paper proposes the route recommender system which supports multi modes of transportation in Bangkok, including BTS, MRT, ARL, BMTA bus, and Chaophraya Riverboat. Users can see suggested routes and sort routes by travel time, fare, number of transfer, and overall score. The A* algorithm with the Haversine formula as the heuristic function is used to calculate the possible routes. Then the best route is selected based on the score, which is calculated form four factors: travel time, fare, number of transfer, and distance. The database contains 13,510 stops, and the results show that the system can suggest accurate routes within a few seconds, which is fast enough for all use cases and achieved overall user satisfaction at 84.8% from our user experience survey.